KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

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International Conference on Information and Kno...(2022)

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TL;DR

A fully-observed dataset from Kuaishou's online environment, where almost all 1,411 users have been exposed to all 3,327 items, and a preliminary analysis of how the two factors - data density and exposure bias - affect the evaluation results of multi-round conversational recommendation.

Abstract

The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This issue is usually approached by utilizing the interaction history to conduct offline evaluation. However, existing datasets of user-item interactions are partially observed, leaving it unclear how and to what extent the missing interactions will influence the evaluation. To answer this question, we collect a fully-observed dataset from Kuaishou's online environment, where almost all 1,411 users have been exposed to all 3,327 items. To the best of our knowledge, this is the first real-world fully-observed data with millions of user-item interactions. With this unique dataset, we conduct a preliminary analysis of how the two factors - data density and exposure bias - affect the evaluation results of multi-round conversational recommendation. Our main discoveries are that the performance ranking of different methods varies with the two factors, and this effect can only be alleviated in certain cases by estimating missing interactions for user simulation. This demonstrates the necessity of the fully-observed dataset. We release the dataset and the pipeline implementation for evaluation at https://kuairec.com

Authors

Xiangnan He

12 papers

Tat-seng Chua

9 papers

Wenqiang Lei

4 papers

References77 items

Variational Reasoning about User Preferences for Conversational Recommendation

User-Centric Conversational Recommendation with Multi-Aspect User Modeling

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

Who are the Best Adopters? User Selection Model for Free Trial Item Promotion

C²-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

Published in

International Conference on Information and Kno...(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Xiangnan He

12 papers

Tat-seng Chua

9 papers

Wenqiang Lei

4 papers

References77 items

Variational Reasoning about User Preferences for Conversational Recommendation

User-Centric Conversational Recommendation with Multi-Aspect User Modeling

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

Who are the Best Adopters? User Selection Model for Free Trial Item Promotion

C²-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

Chongming Gao

2 papers

Jiawei Chen

3 papers

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

RecSys 2021 Tutorial on Conversational Recommendation: Formulation, Methods, and Evaluation

Counterfactual Data-Augmented Sequential Recommendation

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

Causal Intervention for Leveraging Popularity Bias in Recommendation

Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation

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Bias and Debias in Recommender System: A Survey and Future Directions

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"Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue

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Multi-sided Exposure Bias in Recommendation

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Towards Conversational Recommendation over Multi-Type Dialogs

Reward Constrained Interactive Recommendation with Natural Language Feedback

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A Survey on Conversational Recommender Systems

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Conversational Recommender System

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Interactive collaborative filtering

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IR evaluation methods for retrieving highly relevant documents

CIKM ’22, October 17–21, 2022, Atlanta, GA, USA

Field of Study

Computer Science

Journal Information

Name

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Venue Information

Name

International Conference on Information and Knowledge Management

Type

conference

URL

http://www.cikm.org/

Alternate Names

Conference on Information and Knowledge Management
Conf Inf Knowl Manag
Int Conf Inf Knowl Manag
CIKM